Open AccessEnvironmental mixtures and children's health: identifying appropriate statistical approaches
Author(s) -
Eva M. Tanner,
Alison Lee,
Elena Colicino
Publication year - 2020
Publication title -
current opinion in pediatrics, with evaluated medline/current opinion in pediatrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 87
eISSN - 1080-8116
pISSN - 1040-8703
DOI - 10.1097/mop.0000000000000877
Subject(s) - computer science , identification (biology) , data science , context (archaeology) , causal inference , machine learning , bayesian probability , inference , quantile , variable (mathematics) , management science , artificial intelligence , data mining , econometrics , mathematics , paleontology , botany , economics , biology , mathematical analysis
Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context.